Overview

Dataset statistics

Number of variables18
Number of observations6108
Missing cells7195
Missing cells (%)6.5%
Duplicate rows6
Duplicate rows (%)0.1%
Total size in memory859.1 KiB
Average record size in memory144.0 B

Variable types

NUM14
CAT4

Reproduction

Analysis started2020-07-26 10:17:40.319376
Analysis finished2020-07-26 10:18:32.780068
Duration52.46 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Country_Region has constant value "US" Constant
Dataset has 6 (0.1%) duplicate rows Duplicates
Province_State has a high cardinality: 59 distinct values High cardinality
Last_Update has a high cardinality: 107 distinct values High cardinality
Active is highly correlated with ConfirmedHigh correlation
Confirmed is highly correlated with Active and 1 other fieldsHigh correlation
People_Hospitalized is highly correlated with Confirmed and 1 other fieldsHigh correlation
Deaths is highly correlated with People_HospitalizedHigh correlation
ISO3 is highly correlated with Province_StateHigh correlation
Province_State is highly correlated with ISO3High correlation
Lat has 228 (3.7%) missing values Missing
Long_ has 228 (3.7%) missing values Missing
Recovered has 1477 (24.2%) missing values Missing
Incident_Rate has 228 (3.7%) missing values Missing
People_Tested has 228 (3.7%) missing values Missing
People_Hospitalized has 2200 (36.0%) missing values Missing
Mortality_Rate has 123 (2.0%) missing values Missing
Testing_Rate has 228 (3.7%) missing values Missing
Hospitalization_Rate has 2200 (36.0%) missing values Missing
Confirmed has 123 (2.0%) zeros Zeros
Deaths has 240 (3.9%) zeros Zeros
Recovered has 130 (2.1%) zeros Zeros
Active has 94 (1.5%) zeros Zeros
Incident_Rate has 105 (1.7%) zeros Zeros
Mortality_Rate has 117 (1.9%) zeros Zeros

Variables

Province_State
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct count59
Unique (%)1.0%
Missing0
Missing (%)0.0%
Memory size47.7 KiB
Maryland
 
105
Washington
 
105
Arizona
 
105
Kansas
 
105
Kentucky
 
105
Other values (54)
5583
ValueCountFrequency (%) 
Maryland1051.7%
 
Washington1051.7%
 
Arizona1051.7%
 
Kansas1051.7%
 
Kentucky1051.7%
 
South Dakota1051.7%
 
Mississippi1051.7%
 
Hawaii1051.7%
 
Nevada1051.7%
 
Florida1051.7%
 
Other values (49)505882.8%
 
2020-07-26T15:48:33.072705image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length24
Median length8
Mean length9.292239686
Min length4

Country_Region
Categorical

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.7 KiB
US
6108
ValueCountFrequency (%) 
US6108100.0%
 
2020-07-26T15:48:33.274126image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Last_Update
Categorical

HIGH CARDINALITY

Distinct count107
Unique (%)1.8%
Missing19
Missing (%)0.3%
Memory size47.7 KiB
2020-04-30 02:32:31
 
59
2020-04-25 06:32:46
 
59
2020-04-29 02:32:33
 
59
2020-04-27 02:32:46
 
59
2020-04-21 23:40:34
 
59
Other values (102)
5794
ValueCountFrequency (%) 
2020-04-30 02:32:31591.0%
 
2020-04-25 06:32:46591.0%
 
2020-04-29 02:32:33591.0%
 
2020-04-27 02:32:46591.0%
 
2020-04-21 23:40:34591.0%
 
2020-04-26 02:32:45591.0%
 
2020-04-22 23:40:26591.0%
 
2020-04-28 02:32:46591.0%
 
2020-04-24 03:33:00591.0%
 
2020-07-26 04:35:13580.9%
 
Other values (97)550090.0%
 
2020-07-26T15:48:33.483607image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length19
Median length19
Mean length18.95022921
Min length3

Lat
Real number (ℝ)

MISSING

Distinct count58
Unique (%)1.0%
Missing228
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean36.840089285714285
Minimum-14.271
Maximum61.3707
Zeros0
Zeros (%)0.0%
Memory size47.7 KiB
2020-07-26T15:48:33.629876image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-14.271
5-th percentile15.0979
Q134.5946
median39.06185
Q342.36165
95-th percentile47.4009
Maximum61.3707
Range75.6417
Interquartile range (IQR)7.76705

Descriptive statistics

Standard deviation10.79030945
Coefficient of variation (CV)0.2928958551
Kurtosis7.667000536
Mean36.84008929
Median Absolute Deviation (MAD)4.15675
Skewness-2.153915943
Sum216619.725
Variance116.4307781
2020-07-26T15:48:33.777523image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
31.05451051.7%
 
43.32661051.7%
 
35.56531051.7%
 
36.11621051.7%
 
39.84941051.7%
 
38.31351051.7%
 
33.04061051.7%
 
37.76931051.7%
 
38.52661051.7%
 
37.66811051.7%
 
Other values (48)483079.1%
 
(Missing)2283.7%
 
ValueCountFrequency (%) 
-14.2711011.7%
 
-14.27140.1%
 
13.44431051.7%
 
15.09791051.7%
 
18.22081051.7%
 
ValueCountFrequency (%) 
61.37071051.7%
 
47.52891051.7%
 
47.40091051.7%
 
46.92191051.7%
 
45.69451051.7%
 

Long_
Real number (ℝ)

MISSING

Distinct count58
Unique (%)1.0%
Missing228
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean-85.20661431972789
Minimum-170.1322
Maximum145.6739
Zeros0
Zeros (%)0.0%
Memory size47.7 KiB
2020-07-26T15:48:33.916152image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-170.1322
5-th percentile-152.4044
Q1-101.165775
median-87.9442
Q3-76.970625
95-th percentile-64.8963
Maximum145.6739
Range315.8061
Interquartile range (IQR)24.19515

Descriptive statistics

Standard deviation49.3124053
Coefficient of variation (CV)-0.5787391706
Kurtosis14.38077386
Mean-85.20661432
Median Absolute Deviation (MAD)11.6672
Skewness3.415406708
Sum-501014.8922
Variance2431.713316
2020-07-26T15:48:34.041814image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-119.68161051.7%
 
-91.86781051.7%
 
-82.76491051.7%
 
-157.49831051.7%
 
-81.68681051.7%
 
-114.47881051.7%
 
-75.50711051.7%
 
-98.26811051.7%
 
-88.98611051.7%
 
-77.02681051.7%
 
Other values (48)483079.1%
 
(Missing)2283.7%
 
ValueCountFrequency (%) 
-170.13221< 0.1%
 
-170.1321041.7%
 
-157.49831051.7%
 
-152.40441051.7%
 
-122.07091051.7%
 
ValueCountFrequency (%) 
145.67391051.7%
 
144.79371051.7%
 
-64.89631051.7%
 
-66.59011051.7%
 
-69.38191051.7%
 

Confirmed
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count5097
Unique (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34447.20415848068
Minimum0
Maximum446452
Zeros123
Zeros (%)2.0%
Memory size47.7 KiB
2020-07-26T15:48:34.175418image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49
Q12400
median11538
Q337734.75
95-th percentile146184.55
Maximum446452
Range446452
Interquartile range (IQR)35334.75

Descriptive statistics

Standard deviation63444.33794
Coefficient of variation (CV)1.841784827
Kurtosis15.82596407
Mean34447.20416
Median Absolute Deviation (MAD)10917.5
Skewness3.708679636
Sum210403523
Variance4025184017
2020-07-26T15:48:34.296132image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01232.0%
 
491051.7%
 
1031051.7%
 
30230.4%
 
69220.4%
 
14180.3%
 
22120.2%
 
3190.1%
 
6680.1%
 
7270.1%
 
Other values (5087)567692.9%
 
ValueCountFrequency (%) 
01232.0%
 
113< 0.1%
 
133< 0.1%
 
14180.3%
 
153< 0.1%
 
ValueCountFrequency (%) 
4464521< 0.1%
 
4401851< 0.1%
 
4307731< 0.1%
 
4212861< 0.1%
 
4145111< 0.1%
 

Deaths
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count2464
Unique (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1707.6835297969876
Minimum0
Maximum32608
Zeros240
Zeros (%)3.9%
Memory size47.7 KiB
2020-07-26T15:48:34.421800image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q170
median383
Q31465.5
95-th percentile6885.2
Maximum32608
Range32608
Interquartile range (IQR)1395.5

Descriptive statistics

Standard deviation4154.764284
Coefficient of variation (CV)2.432982582
Kurtosis31.02185064
Mean1707.68353
Median Absolute Deviation (MAD)371
Skewness5.18860849
Sum10430531
Variance17262066.26
2020-07-26T15:48:34.545427image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
02403.9%
 
21071.8%
 
51061.7%
 
31021.7%
 
17901.5%
 
6781.3%
 
56500.8%
 
10450.7%
 
16440.7%
 
9380.6%
 
Other values (2454)520885.3%
 
ValueCountFrequency (%) 
02403.9%
 
180.1%
 
21071.8%
 
31021.7%
 
4150.2%
 
ValueCountFrequency (%) 
326081< 0.1%
 
325961< 0.1%
 
325941< 0.1%
 
325581< 0.1%
 
325201< 0.1%
 

Recovered
Real number (ℝ≥0)

MISSING
ZEROS

Distinct count2869
Unique (%)62.0%
Missing1477
Missing (%)24.2%
Infinite0
Infinite (%)0.0%
Mean11816.05031310732
Minimum0.0
Maximum221510.0
Zeros130
Zeros (%)2.1%
Memory size47.7 KiB
2020-07-26T15:48:34.680364image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q1775
median3317
Q313356
95-th percentile55318
Maximum221510
Range221510
Interquartile range (IQR)12581

Descriptive statistics

Standard deviation20449.06161
Coefficient of variation (CV)1.730617344
Kurtosis16.69242177
Mean11816.05031
Median Absolute Deviation (MAD)2951
Skewness3.3304013
Sum54720129
Variance418164120.7
2020-07-26T15:48:34.800048image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01302.1%
 
19380.6%
 
12210.3%
 
61200.3%
 
15642180.3%
 
64160.3%
 
23887160.3%
 
15110.2%
 
51110.2%
 
13110.2%
 
Other values (2859)433971.0%
 
(Missing)147724.2%
 
ValueCountFrequency (%) 
01302.1%
 
92< 0.1%
 
1180.1%
 
12210.3%
 
13110.2%
 
ValueCountFrequency (%) 
2215101< 0.1%
 
2122161< 0.1%
 
2038261< 0.1%
 
1953151< 0.1%
 
1865291< 0.1%
 

Active
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct count4778
Unique (%)78.4%
Missing17
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean24064.14644557544
Minimum-120720.0
Maximum438044.0
Zeros94
Zeros (%)1.5%
Memory size47.7 KiB
2020-07-26T15:48:34.922719image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-120720
5-th percentile20
Q1852.5
median6450
Q321021.5
95-th percentile124380
Maximum438044
Range558764
Interquartile range (IQR)20169

Descriptive statistics

Standard deviation51953.95978
Coefficient of variation (CV)2.158977876
Kurtosis17.37413245
Mean24064.14645
Median Absolute Deviation (MAD)6202
Skewness3.850806333
Sum146574716
Variance2699213937
2020-07-26T15:48:35.045391image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
491061.7%
 
100961.6%
 
0941.5%
 
2340.6%
 
9300.5%
 
12210.3%
 
7200.3%
 
23180.3%
 
103150.2%
 
11140.2%
 
Other values (4768)564392.4%
 
(Missing)170.3%
 
ValueCountFrequency (%) 
-1207201< 0.1%
 
-1159361< 0.1%
 
-1114241< 0.1%
 
-1069881< 0.1%
 
-1003721< 0.1%
 
ValueCountFrequency (%) 
4380441< 0.1%
 
4318481< 0.1%
 
4225721< 0.1%
 
4132391< 0.1%
 
4087341< 0.1%
 

FIPS
Real number (ℝ≥0)

Distinct count60
Unique (%)1.0%
Missing19
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean3257.9022828050583
Minimum1.0
Maximum99999.0
Zeros0
Zeros (%)0.0%
Memory size47.7 KiB
2020-07-26T15:48:35.167065image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q118
median32
Q348
95-th percentile78
Maximum99999
Range99998
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17180.88557
Coefficient of variation (CV)5.273603713
Kurtosis24.74004635
Mean3257.902283
Median Absolute Deviation (MAD)15
Skewness5.159946349
Sum19837367
Variance295182829.1
2020-07-26T15:48:35.285748image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
661051.7%
 
201051.7%
 
541051.7%
 
481051.7%
 
441051.7%
 
721051.7%
 
401051.7%
 
361051.7%
 
321051.7%
 
301051.7%
 
Other values (50)503982.5%
 
ValueCountFrequency (%) 
11051.7%
 
21051.7%
 
41051.7%
 
51051.7%
 
61051.7%
 
ValueCountFrequency (%) 
999991041.7%
 
888881041.7%
 
9991< 0.1%
 
8881< 0.1%
 
781041.7%
 

Incident_Rate
Real number (ℝ≥0)

MISSING
ZEROS

Distinct count5398
Unique (%)91.8%
Missing228
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean505.3689272359527
Minimum0.0
Maximum2231.417438587298
Zeros105
Zeros (%)1.7%
Memory size47.7 KiB
2020-07-26T15:48:35.425548image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48.85040923
Q1144.9333641
median335.1103651
Q3716.7461476
95-th percentile1555.968115
Maximum2231.417439
Range2231.417439
Interquartile range (IQR)571.8127836

Descriptive statistics

Standard deviation478.1219857
Coefficient of variation (CV)0.9460850477
Kurtosis1.190478593
Mean505.3689272
Median Absolute Deviation (MAD)233.769541
Skewness1.346515774
Sum2971569.292
Variance228600.6332
2020-07-26T15:48:35.544231image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01051.7%
 
54.40301755230.4%
 
64.32486855220.4%
 
25.38807486180.3%
 
39.89554621120.2%
 
56.2164514790.1%
 
61.5281351480.1%
 
67.1216019770.1%
 
44.8175392860.1%
 
93.7715019960.1%
 
Other values (5388)566492.7%
 
(Missing)2283.7%
 
ValueCountFrequency (%) 
01051.7%
 
19.94777312< 0.1%
 
19.94777311< 0.1%
 
23.574640943< 0.1%
 
25.38807486180.3%
 
ValueCountFrequency (%) 
2231.4174392< 0.1%
 
2198.7528851< 0.1%
 
2186.5886081< 0.1%
 
2147.3702031< 0.1%
 
2137.1994541< 0.1%
 

People_Tested
Real number (ℝ≥0)

MISSING

Distinct count5479
Unique (%)93.2%
Missing228
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean373914.58554421767
Minimum3.0
Maximum7047355.0
Zeros0
Zeros (%)0.0%
Memory size47.7 KiB
2020-07-26T15:48:35.665036image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4085.7
Q151177.25
median151820
Q3415379.25
95-th percentile1388184.55
Maximum7047355
Range7047352
Interquartile range (IQR)364202

Descriptive statistics

Standard deviation671985.5117
Coefficient of variation (CV)1.797163143
Kurtosis27.85742761
Mean373914.5855
Median Absolute Deviation (MAD)126593.5
Skewness4.605159266
Sum2198617763
Variance4.515645279e+11
2020-07-26T15:48:35.795690image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
174290.5%
 
3190.3%
 
696140.2%
 
8217100.2%
 
1037100.2%
 
816990.1%
 
6590.1%
 
12480.1%
 
10580.1%
 
81680.1%
 
Other values (5469)575694.2%
 
(Missing)2283.7%
 
ValueCountFrequency (%) 
3190.3%
 
382< 0.1%
 
403< 0.1%
 
551< 0.1%
 
562< 0.1%
 
ValueCountFrequency (%) 
70473551< 0.1%
 
69158761< 0.1%
 
67783041< 0.1%
 
66644191< 0.1%
 
65369321< 0.1%
 

People_Hospitalized
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct count2462
Unique (%)63.0%
Missing2200
Missing (%)36.0%
Infinite0
Infinite (%)0.0%
Mean5252.702405322416
Minimum2.0
Maximum89995.0
Zeros0
Zeros (%)0.0%
Memory size47.7 KiB
2020-07-26T15:48:35.921353image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile69
Q1465.75
median1629
Q34665
95-th percentile15097.7
Maximum89995
Range89993
Interquartile range (IQR)4199.25

Descriptive statistics

Standard deviation13150.54949
Coefficient of variation (CV)2.503577869
Kurtosis29.1253213
Mean5252.702405
Median Absolute Deviation (MAD)1416
Skewness5.283137846
Sum20527561
Variance172936952
2020-07-26T15:48:36.041033image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
89995530.9%
 
4170.3%
 
83150.2%
 
4389130.2%
 
67110.2%
 
291110.2%
 
331100.2%
 
65100.2%
 
5285100.2%
 
82100.2%
 
Other values (2452)374861.4%
 
(Missing)220036.0%
 
ValueCountFrequency (%) 
240.1%
 
350.1%
 
4170.3%
 
63< 0.1%
 
92< 0.1%
 
ValueCountFrequency (%) 
89995530.9%
 
898611< 0.1%
 
897031< 0.1%
 
895901< 0.1%
 
894001< 0.1%
 

Mortality_Rate
Real number (ℝ≥0)

MISSING
ZEROS

Distinct count5354
Unique (%)89.5%
Missing123
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.9999160473657813
Minimum0.0
Maximum70.37037037037038
Zeros117
Zeros (%)1.9%
Memory size47.7 KiB
2020-07-26T15:48:36.156723image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.087620167
Q12.309468822
median3.733554581
Q35.121699088
95-th percentile8.129773028
Maximum70.37037037
Range70.37037037
Interquartile range (IQR)2.812230266

Descriptive statistics

Standard deviation2.836388524
Coefficient of variation (CV)0.709112014
Kurtosis182.2756813
Mean3.999916047
Median Absolute Deviation (MAD)1.407546285
Skewness8.883043544
Sum23939.49754
Variance8.04509986
2020-07-26T15:48:36.283384image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01171.9%
 
2.912621359951.6%
 
6.666666667240.4%
 
8.695652174210.3%
 
14.28571429180.3%
 
9.090909091120.2%
 
6.060606061110.2%
 
6.45161290390.1%
 
3.44827586270.1%
 
5.26315789570.1%
 
Other values (5344)566492.7%
 
(Missing)1232.0%
 
ValueCountFrequency (%) 
01171.9%
 
0.34843205571< 0.1%
 
0.3546099291< 0.1%
 
0.36363636361< 0.1%
 
0.40562839891< 0.1%
 
ValueCountFrequency (%) 
70.370370371< 0.1%
 
66.666666671< 0.1%
 
65.384615381< 0.1%
 
61.538461542< 0.1%
 
18.181818181< 0.1%
 

UID
Real number (ℝ≥0)

Distinct count59
Unique (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76783480.58251473
Minimum16.0
Maximum84099999.0
Zeros0
Zeros (%)0.0%
Memory size47.7 KiB
2020-07-26T15:48:36.414035image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile580
Q184000012
median84000028
Q384000042
95-th percentile84000056
Maximum84099999
Range84099983
Interquartile range (IQR)30

Descriptive statistics

Standard deviation23547597.39
Coefficient of variation (CV)0.3066753059
Kurtosis6.73480465
Mean76783480.58
Median Absolute Deviation (MAD)16
Skewness-2.955096006
Sum4.689934994e+11
Variance5.54489343e+14
2020-07-26T15:48:36.535710image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
840000511051.7%
 
840000181051.7%
 
840000391051.7%
 
840000241051.7%
 
840000081051.7%
 
840000481051.7%
 
840000321051.7%
 
840000171051.7%
 
840000401051.7%
 
840000251051.7%
 
Other values (49)505882.8%
 
ValueCountFrequency (%) 
161051.7%
 
3161051.7%
 
5801051.7%
 
6301051.7%
 
8501051.7%
 
ValueCountFrequency (%) 
840999991051.7%
 
840888881051.7%
 
84070001180.3%
 
840000561051.7%
 
840000551051.7%
 

ISO3
Categorical

HIGH CORRELATION

Distinct count6
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.7 KiB
USA
5583
PRI
 
105
GUM
 
105
MNP
 
105
ASM
 
105
ValueCountFrequency (%) 
USA558391.4%
 
PRI1051.7%
 
GUM1051.7%
 
MNP1051.7%
 
ASM1051.7%
 
VIR1051.7%
 
2020-07-26T15:48:36.694286image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Testing_Rate
Real number (ℝ≥0)

MISSING

Distinct count5510
Unique (%)93.7%
Missing228
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean6251.6253642461315
Minimum5.3917075537822825
Maximum28356.304534681338
Zeros0
Zeros (%)0.0%
Memory size47.7 KiB
2020-07-26T15:48:36.807981image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum5.391707554
5-th percentile739.5992692
Q12286.517211
median5021.443427
Q39009.045127
95-th percentile15788.9655
Maximum28356.30453
Range28350.91283
Interquartile range (IQR)6722.527915

Descriptive statistics

Standard deviation4967.913382
Coefficient of variation (CV)0.7946594833
Kurtosis1.284749376
Mean6251.625364
Median Absolute Deviation (MAD)3109.553604
Skewness1.169618172
Sum36759557.14
Variance24680163.37
2020-07-26T15:48:36.921677image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
312.7190381290.5%
 
5.391707554170.3%
 
1250.876152130.2%
 
14900.98651100.2%
 
1863.733578100.2%
 
14813.9416890.1%
 
117.873204790.1%
 
188.709764480.1%
 
222.857245680.1%
 
1466.54445580.1%
 
Other values (5500)575994.3%
 
(Missing)2283.7%
 
ValueCountFrequency (%) 
5.391707554170.3%
 
5.3917075542< 0.1%
 
67.089201371< 0.1%
 
68.91048891< 0.1%
 
68.91048891< 0.1%
 
ValueCountFrequency (%) 
28356.304531< 0.1%
 
28031.905081< 0.1%
 
27988.937351< 0.1%
 
27595.657171< 0.1%
 
27417.58881< 0.1%
 

Hospitalization_Rate
Real number (ℝ≥0)

MISSING

Distinct count3789
Unique (%)97.0%
Missing2200
Missing (%)36.0%
Infinite0
Infinite (%)0.0%
Mean12.955935143257337
Minimum1.4184397163120568
Maximum38.501189532117365
Zeros0
Zeros (%)0.0%
Memory size47.7 KiB
2020-07-26T15:48:37.041357image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1.418439716
5-th percentile6.103887652
Q19.028964457
median11.96966046
Q316.29782086
95-th percentile22.63829228
Maximum38.50118953
Range37.08274982
Interquartile range (IQR)7.268856399

Descriptive statistics

Standard deviation5.335575767
Coefficient of variation (CV)0.4118248284
Kurtosis0.7444485609
Mean12.95593514
Median Absolute Deviation (MAD)3.570612648
Skewness0.826037297
Sum50631.79454
Variance28.46836876
2020-07-26T15:48:37.156050image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
11.7647058860.1%
 
13.5699373760.1%
 
13.4053582750.1%
 
10.5263157950.1%
 
13.0637636140.1%
 
4.41176470640.1%
 
11.8827614640.1%
 
10.8108108140.1%
 
13.537117940.1%
 
22.285249483< 0.1%
 
Other values (3779)386363.2%
 
(Missing)220036.0%
 
ValueCountFrequency (%) 
1.4184397163< 0.1%
 
1.4388489211< 0.1%
 
2.1276595741< 0.1%
 
2.2058823533< 0.1%
 
2.3028963011< 0.1%
 
ValueCountFrequency (%) 
38.501189531< 0.1%
 
37.23679351< 0.1%
 
35.777027031< 0.1%
 
34.721311481< 0.1%
 
33.141683781< 0.1%
 

Interactions

2020-07-26T15:47:58.326035image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:47:58.557412image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:47:58.721972image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:47:58.890522image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:47:59.079062image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:47:59.258574image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:47:59.442083image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:47:59.615624image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:47:59.786166image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:47:59.955713image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:00.118240image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:00.309727image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:00.486255image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:00.678740image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:00.870228image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:01.059722image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:01.289140image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:01.452709image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:01.618266image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:01.954364image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:02.159813image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:02.331359image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:02.494921image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:02.665465image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:02.838006image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:02.999572image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:03.157111image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:03.342614image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:03.522134image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:03.682705image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:03.853250image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:04.032770image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:04.210294image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:04.363919image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:04.500553image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:04.633200image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:04.762860image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:04.926378image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:05.073981image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:05.212610image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:05.348247image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:05.502836image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:05.662409image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:05.825973image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:05.991529image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:06.166063image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:06.350568image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:06.508147image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:06.670712image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:06.856216image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:07.014791image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:07.178355image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:07.341916image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:07.505479image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:07.663057image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:07.827617image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:07.999158image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:08.152748image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:08.308331image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:08.453942image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:08.606534image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:08.751147image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:08.900747image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:09.048352image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:09.175051image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:09.307701image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:09.445328image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:09.785379image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:09.928995image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:10.081588image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:10.245151image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:10.396743image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:10.532380image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:10.672009image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:10.833578image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:11.000133image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:11.169716image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:11.306351image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:11.435008image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:11.569646image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:11.699300image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:11.838927image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:11.996501image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:12.128150image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:12.267778image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:12.421369image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:12.569970image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:12.702616image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:12.843239image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:12.982866image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:13.116510image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:13.252145image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:13.405697image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:13.579232image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:13.738805image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:13.901371image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:14.039998image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:14.208551image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:14.401034image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:14.560607image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:14.712202image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:14.860804image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:15.012399image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:15.157012image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:15.309604image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:15.466182image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:15.613790image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:15.767379image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:15.915982image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:16.070568image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:16.213187image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:16.366776image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:16.526349image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:16.692904image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:16.860455image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:17.017036image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:17.179601image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:17.336183image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:17.496753image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:17.663307image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:17.818928image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:17.972516image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:18.109164image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:18.259751image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:18.404364image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:18.548977image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:18.918950image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:19.084506image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:19.242124image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:19.385741image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:19.533345image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:19.689887image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:19.840484image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:19.991081image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:20.142677image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:20.296265image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:20.436887image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:20.565543image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:20.693201image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:20.828839image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:20.982429image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:21.141006image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:21.323517image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:21.488077image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:21.677571image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:21.844125image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:22.013672image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:22.190199image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:22.356753image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:22.534277image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:22.688864image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:22.865393image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:23.024007image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:23.166622image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:23.316204image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:23.486729image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:23.629349image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:23.772965image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:23.932539image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:24.102120image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:24.263687image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:24.425253image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:24.562888image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:24.698525image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:24.833165image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:24.970807image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:25.098458image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:25.234056image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:25.399653image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:25.545264image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:25.691876image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:25.836485image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:25.986085image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:26.129698image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:26.272317image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:26.425905image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:26.564538image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:26.710149image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:26.853766image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:27.001330image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:27.153922image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:27.310503image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:27.479052image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:27.641617image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:27.811163image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:27.978715image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:28.148261image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:28.310867image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:28.461462image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:28.616049image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:28.769637image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:28.925219image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:29.072824image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:29.214446image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:29.364056image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:29.515644image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-07-26T15:48:37.308642image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-07-26T15:48:37.603852image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-07-26T15:48:37.893079image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-07-26T15:48:38.192279image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-07-26T15:48:38.460561image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-07-26T15:48:30.876625image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:31.487331image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:31.883270image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-26T15:48:32.553667image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

Province_StateCountry_RegionLast_UpdateLatLong_ConfirmedDeathsRecoveredActiveFIPSIncident_RatePeople_TestedPeople_HospitalizedMortality_RateUIDISO3Testing_RateHospitalization_Rate
0AlabamaUS2020-04-12 23:18:1532.3182-86.9023356393NaN3470.01.075.98802021583.0437.02.61016084000001.0USA460.30015212.264945
1AlaskaUS2020-04-12 23:18:1561.3707-152.4044272866.0264.02.045.5040498038.031.02.94117684000002.0USA1344.71157611.397059
2ArizonaUS2020-04-12 23:18:1533.7298-111.43123542115NaN3427.04.048.66242242109.0NaN3.24675384000004.0USA578.522286NaN
3ArkansasUS2020-04-12 23:18:1534.9697-92.3731128027367.01253.05.049.43942319722.0130.02.10937584000005.0USA761.75335410.156250
4CaliforniaUS2020-04-12 23:18:1536.1162-119.681622795640NaN22155.06.058.137726190328.05234.02.81202084000006.0USA485.42386822.961176
5ColoradoUS2020-04-12 23:18:1539.0598-105.31117307289NaN7018.08.0128.94372934873.01376.03.95511284000008.0USA615.38999118.831258
6ConnecticutUS2020-04-12 23:18:1541.5978-72.755412035554NaN11481.09.0337.56048341220.01654.04.60324184000009.0USA1156.14815913.743249
7DelawareUS2020-04-12 23:18:1539.3185-75.5071162535191.01590.010.0166.87821711103.0190.02.15384684000010.0USA1140.21467211.692308
8Diamond PrincessUS2020-04-12 23:18:15NaNNaN4900.049.0888.0NaNNaNNaN0.00000084088888.0USANaNNaN
9District of ColumbiaUS2020-04-12 23:18:1538.8974-77.0268187550493.01825.011.0265.67519010640.0NaN2.66666784000011.0USA1507.618148NaN

Last rows

Province_StateCountry_RegionLast_UpdateLatLong_ConfirmedDeathsRecoveredActiveFIPSIncident_RatePeople_TestedPeople_HospitalizedMortality_RateUIDISO3Testing_RateHospitalization_Rate
6098TennesseeUS2020-07-26 04:35:1335.7478-86.69239079696453808.036024.047.01329.5312141332374.04196.01.06172184000047.0USA19510.0315214.621349
6099TexasUS2020-07-26 04:35:1331.0545-97.56353902864990221510.0163786.048.01346.0049723306042.0NaN1.27855084000048.0USA11401.764271NaN
6100UtahUS2020-07-26 04:35:1340.1500-111.86243762327424390.012959.049.01173.533777500139.02213.00.72827884000049.0USA15600.2979455.882040
6101VermontUS2020-07-26 04:35:1344.0459-72.71071396561182.0158.050.0223.72189388816.0NaN4.01146184000050.0USA14233.584246NaN
6102Virgin IslandsUS2020-07-26 04:35:1318.3358-64.89633527236.0109.078.0328.1500548253.0NaN1.988636850.0VIR7693.813626NaN
6103VirginiaUS2020-07-26 04:35:1337.7693-78.170083609207510800.070734.051.0979.5420761010443.012001.02.48179084000051.0USA11838.09678114.353718
6104WashingtonUS2020-07-26 04:35:1347.4009-121.4905518491494NaN50355.053.0680.889410883982.05301.02.88144484000053.0USA11608.59384410.223919
6105West VirginiaUS2020-07-26 04:35:1338.4912-80.954557751034115.01557.054.0322.239191256914.0NaN1.78355084000054.0USA14335.542788NaN
6106WisconsinUS2020-07-26 04:35:1344.2685-89.61654787089137287.09692.055.0822.164751860243.04368.01.86129184000055.0USA14774.6286189.124713
6107WyomingUS2020-07-26 04:35:1342.7560-107.30252446251866.0555.056.0422.62841748269.0158.01.02207784000056.0USA8340.0862886.459526